


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_snake_case	:						Union[str, Any]         =							logging.get_logger(__name__)

_snake_case	:						Tuple         =							{"vocab_file": "spm_char.model"}

_snake_case	:						str         =							{
    "vocab_file": {
        "microsoft/speecht5_asr": "https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model",
        "microsoft/speecht5_tts": "https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model",
        "microsoft/speecht5_vc": "https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model",
    }
}

_snake_case	:						Tuple         =							{
    "microsoft/speecht5_asr": 1_024,
    "microsoft/speecht5_tts": 1_024,
    "microsoft/speecht5_vc": 1_024,
}

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				str	    =    VOCAB_FILES_NAMES
							__UpperCAmelCase       :				Union[str, Any]	    =    PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase       :				Tuple	    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase       :				Dict	    =    ["input_ids", "attention_mask"]

							def __init__(				self			:    Optional[Any]       ,      lowerCamelCase			:    Optional[int]       ,      lowerCamelCase			:    str="<s>"       ,      lowerCamelCase			:    Union[str, Any]="</s>"       ,      lowerCamelCase			:    Any="<unk>"       ,      lowerCamelCase			:    Union[str, Any]="<pad>"       ,      lowerCamelCase			:    Optional[Dict[str, Any]] = None       ,      **lowerCamelCase			:    Any       ,      )      ->       None:
														__snake_case						:     Optional[int]									=  {} if sp_model_kwargs is None else sp_model_kwargs

														super().__init__(
														    bos_token=lowerCamelCase       ,      eos_token=lowerCamelCase       ,      unk_token=lowerCamelCase       ,      pad_token=lowerCamelCase       ,      sp_model_kwargs=self.sp_model_kwargs       ,      **lowerCamelCase       ,      )

														__snake_case						:     Dict									=  vocab_file

														__snake_case						:     List[Any]									=  spm.SentencePieceProcessor(**self.sp_model_kwargs							)
														self.sp_model.Load(lowerCamelCase							)

							@property
							def        __snake_case   (				self			:    int							)      ->       Tuple:
														return self.sp_model.get_piece_size()

							def        __snake_case   (				self			:    List[str]							)      ->       Any:
														__snake_case						:     List[str]									=  {self.convert_ids_to_tokens(lowerCamelCase							): i for i in range(self.vocab_size							)}
														vocab.update(self.added_tokens_encoder							)
														return vocab

							def __getstate__(				self			:    int							)      ->       Union[str, Any]:
														__snake_case						:     Optional[int]									=  self.__dict__.copy()
														__snake_case						:     Optional[Any]									=  None
														return state

							def __setstate__(				self			:    int       ,      lowerCamelCase			:    str							)      ->       Tuple:
														__snake_case						:     Tuple									=  d

														# for backward compatibility
														if not hasattr(self       ,      "sp_model_kwargs"							):
																					__snake_case						:     Tuple									=  {}

														__snake_case						:     Dict									=  spm.SentencePieceProcessor(**self.sp_model_kwargs							)
														self.sp_model.Load(self.vocab_file							)

							def        __snake_case   (				self			:    List[str]       ,      lowerCamelCase			:    str							)      ->       List[str]:
														return self.sp_model.encode(lowerCamelCase       ,      out_type=lowerCamelCase							)

							def        __snake_case   (				self			:    Union[str, Any]       ,      lowerCamelCase			:    Union[str, Any]							)      ->       Optional[int]:
														return self.sp_model.piece_to_id(lowerCamelCase							)

							def        __snake_case   (				self			:    Tuple       ,      lowerCamelCase			:    Dict							)      ->       Tuple:
														__snake_case						:     List[Any]									=  self.sp_model.IdToPiece(lowerCamelCase							)
														return token

							def        __snake_case   (				self			:    int       ,      lowerCamelCase			:    Any							)      ->       Tuple:
														__snake_case						:     Optional[Any]									=  []
														__snake_case						:     Tuple									=  ""
														for token in tokens:
																					# make sure that special tokens are not decoded using sentencepiece model
																					if token in self.all_special_tokens:
																												out_string += self.sp_model.decode(lowerCamelCase							) + token
																												__snake_case						:     Tuple									=  []
																					else:
																												current_sub_tokens.append(lowerCamelCase							)
														out_string += self.sp_model.decode(lowerCamelCase							)
														return out_string.strip()

							def        __snake_case   (				self			:    Any       ,      lowerCamelCase			:    Optional[Any]       ,      lowerCamelCase			:    Optional[Any]=None							)      ->       List[int]:
														if token_ids_a is None:
																					return token_ids_a + [self.eos_token_id]
														# We don't expect to process pairs, but leave the pair logic for API consistency
														return token_ids_a + token_ids_a + [self.eos_token_id]

							def        __snake_case   (				self			:    Optional[Any]       ,      lowerCamelCase			:    List[int]       ,      lowerCamelCase			:    Optional[List[int]] = None       ,      lowerCamelCase			:    bool = False							)      ->       List[int]:
														if already_has_special_tokens:
																					return super().get_special_tokens_mask(
																					    token_ids_a=lowerCamelCase       ,      token_ids_a=lowerCamelCase       ,      already_has_special_tokens=lowerCamelCase							)

														__snake_case						:     str									=  [1]
														if token_ids_a is None:
																					return ([0] * len(lowerCamelCase							)) + suffix_ones
														return ([0] * len(lowerCamelCase							)) + ([0] * len(lowerCamelCase							)) + suffix_ones






							def        __snake_case   (				self			:    Any       ,      lowerCamelCase			:    str       ,      lowerCamelCase			:    Optional[str] = None							)      ->       Tuple[str]:
														if not os.path.isdir(lowerCamelCase							):
																					logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
																					return
														__snake_case						:     Optional[int]									=  os.path.join(
														    lowerCamelCase       ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

														if os.path.abspath(self.vocab_file							) != os.path.abspath(lowerCamelCase							) and os.path.isfile(self.vocab_file							):
																					copyfile(self.vocab_file       ,      lowerCamelCase							)
														elif not os.path.isfile(self.vocab_file							):
																					with open(lowerCamelCase       ,      "wb"							) as fi:
																												__snake_case						:     int									=  self.sp_model.serialized_model_proto()
																												fi.write(lowerCamelCase							)

														return (out_vocab_file,)









'''simple docstring'''





from __future__ import annotations

import os
from collections.abc import Mapping

_UpperCamelCase						:						Any  =					tuple[int, int]


class  snake_case__      :




					def __init__(  self  :		List[str]				, _A  :		set[int]				, _A  :		Mapping[EdgeT, int]      )    ->							None:
								UpperCAmelCase_      :       set[int]          =       vertices
								UpperCAmelCase_      :       dict[EdgeT, int]          =       {
								    (min(_A      ), max(_A      )): weight for edge, weight in edges.items()
								}




					def 		A      (  self  :		Union[str, Any]				, _A  :		EdgeT				, _A  :		int      )    ->							None:
								self.vertices.add(edge[0]      )
								self.vertices.add(edge[1]      )
								UpperCAmelCase_      :       List[str]          =       weight





					def 		A      (  self  :		str      )    ->							Graph:
								UpperCAmelCase_      :       Graph          =       Graph({min(self.vertices      )}				, {}      )
								UpperCAmelCase_      :       EdgeT
								UpperCAmelCase_      :       int
								UpperCAmelCase_      :       EdgeT
								UpperCAmelCase_      :       int

								while len(subgraph.vertices      ) < len(self.vertices      ):
											UpperCAmelCase_      :       Optional[Any]          =       max(self.edges.values()      ) + 1
											for edge, weight in self.edges.items():
														if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
																	if weight < min_weight:
																				UpperCAmelCase_      :       Dict          =       edge
																				UpperCAmelCase_      :       Tuple          =       weight

											subgraph.add_edge(_A				, _A      )

								return subgraph






def __UpperCAmelCase   (					A					:			str = "p107_network.txt"		)    ->   int:
			UpperCAmelCase_      :       str          =       os.path.abspath(os.path.dirname(A		)		)
			UpperCAmelCase_      :       str          =       os.path.join(A			,       A		)
			UpperCAmelCase_      :       dict[EdgeT, int]          =       {}
			UpperCAmelCase_      :       list[str]
			UpperCAmelCase_      :       int
			UpperCAmelCase_      :       int

			with open(A		) as f:
						UpperCAmelCase_      :       int          =       f.read().strip().split('''\n'''		)

			UpperCAmelCase_      :       Any          =       [line.split(''','''		) for line in data]

			for edgea in range(1			,       len(A		)		):
						for edgea in range(A		):
									if adjaceny_matrix[edgea][edgea] != "-":
												UpperCAmelCase_      :       Optional[int]          =       int(adjaceny_matrix[edgea][edgea]		)

			UpperCAmelCase_      :       Graph          =       Graph(set(range(len(A		)		)		)			,       A		)

			UpperCAmelCase_      :       Graph          =       graph.prims_algorithm()

			UpperCAmelCase_      :       int          =       sum(graph.edges.values()		)
			UpperCAmelCase_      :       int          =       sum(subgraph.edges.values()		)

			return initial_total - optimal_total


if __name__ == "__main__":
						print(f'''{solution() = }''')


